355 lines
14 KiB
Python
355 lines
14 KiB
Python
# Copyright 2020-2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""
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Testing RandomSharpness op in DE
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"""
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import numpy as np
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import mindspore.dataset as ds
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import mindspore.dataset.transforms
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import mindspore.dataset.vision as vision
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from mindspore import log as logger
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from util import visualize_list, visualize_one_channel_dataset, diff_mse, save_and_check_md5, \
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config_get_set_seed, config_get_set_num_parallel_workers
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DATA_DIR = "../data/dataset/testImageNetData/train/"
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MNIST_DATA_DIR = "../data/dataset/testMnistData"
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GENERATE_GOLDEN = False
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def test_random_sharpness_py(degrees=(0.7, 0.7), plot=False):
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"""
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Feature: RandomSharpness op
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Description: Test RandomSharpness with Python implementation
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Expectation: The dataset is processed as expected
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"""
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logger.info("Test RandomSharpness Python implementation")
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# Original Images
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data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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transforms_original = mindspore.dataset.transforms.Compose([vision.Decode(True),
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vision.Resize((224, 224)),
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vision.ToTensor()])
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ds_original = data.map(operations=transforms_original, input_columns="image")
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ds_original = ds_original.batch(512)
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for idx, (image, _) in enumerate(ds_original.create_tuple_iterator(num_epochs=1, output_numpy=True)):
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if idx == 0:
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images_original = np.transpose(image, (0, 2, 3, 1))
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else:
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images_original = np.append(images_original,
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np.transpose(image, (0, 2, 3, 1)),
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axis=0)
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# Random Sharpness Adjusted Images
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data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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py_op = vision.RandomSharpness()
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if degrees is not None:
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py_op = vision.RandomSharpness(degrees)
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transforms_random_sharpness = mindspore.dataset.transforms.Compose([vision.Decode(True),
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vision.Resize((224, 224)),
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py_op,
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vision.ToTensor()])
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ds_random_sharpness = data.map(operations=transforms_random_sharpness, input_columns="image")
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ds_random_sharpness = ds_random_sharpness.batch(512)
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for idx, (image, _) in enumerate(ds_random_sharpness.create_tuple_iterator(num_epochs=1, output_numpy=True)):
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if idx == 0:
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images_random_sharpness = np.transpose(image, (0, 2, 3, 1))
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else:
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images_random_sharpness = np.append(images_random_sharpness,
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np.transpose(image, (0, 2, 3, 1)),
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axis=0)
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num_samples = images_original.shape[0]
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mse = np.zeros(num_samples)
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for i in range(num_samples):
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mse[i] = diff_mse(images_random_sharpness[i], images_original[i])
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logger.info("MSE= {}".format(str(np.mean(mse))))
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if plot:
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visualize_list(images_original, images_random_sharpness)
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def test_random_sharpness_py_md5():
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"""
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Feature: RandomSharpness op
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Description: Test RandomSharpness with Python implementation with md5 comparison
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Expectation: The dataset is processed as expected
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"""
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logger.info("Test RandomSharpness Python implementation with md5 comparison")
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original_seed = config_get_set_seed(5)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# define map operations
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transforms = [
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vision.Decode(True),
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vision.RandomSharpness((20.0, 25.0)),
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vision.ToTensor()
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]
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transform = mindspore.dataset.transforms.Compose(transforms)
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# Generate dataset
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data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data = data.map(operations=transform, input_columns=["image"])
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# check results with md5 comparison
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filename = "random_sharpness_py_01_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore configuration
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ds.config.set_seed(original_seed)
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ds.config.set_num_parallel_workers(original_num_parallel_workers)
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def test_random_sharpness_c(degrees=(1.6, 1.6), plot=False):
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"""
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Feature: RandomSharpness op
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Description: Test RandomSharpness with cpp op
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Expectation: The dataset is processed as expected
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"""
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print(degrees)
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logger.info("Test RandomSharpness cpp op")
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# Original Images
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data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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transforms_original = [vision.Decode(),
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vision.Resize((224, 224))]
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ds_original = data.map(operations=transforms_original, input_columns="image")
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ds_original = ds_original.batch(512)
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for idx, (image, _) in enumerate(ds_original.create_tuple_iterator(num_epochs=1, output_numpy=True)):
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if idx == 0:
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images_original = image
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else:
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images_original = np.append(images_original,
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image,
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axis=0)
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# Random Sharpness Adjusted Images
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data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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c_op = vision.RandomSharpness()
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if degrees is not None:
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c_op = vision.RandomSharpness(degrees)
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transforms_random_sharpness = [vision.Decode(),
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vision.Resize((224, 224)),
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c_op]
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ds_random_sharpness = data.map(operations=transforms_random_sharpness, input_columns="image")
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ds_random_sharpness = ds_random_sharpness.batch(512)
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for idx, (image, _) in enumerate(ds_random_sharpness.create_tuple_iterator(num_epochs=1, output_numpy=True)):
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if idx == 0:
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images_random_sharpness = image
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else:
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images_random_sharpness = np.append(images_random_sharpness,
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image,
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axis=0)
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num_samples = images_original.shape[0]
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mse = np.zeros(num_samples)
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for i in range(num_samples):
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mse[i] = diff_mse(images_random_sharpness[i], images_original[i])
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logger.info("MSE= {}".format(str(np.mean(mse))))
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if plot:
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visualize_list(images_original, images_random_sharpness)
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def test_random_sharpness_c_md5():
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"""
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Feature: RandomSharpness op
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Description: Test RandomSharpness with cpp op with md5 comparison
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Expectation: The dataset is processed as expected
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"""
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logger.info("Test RandomSharpness cpp op with md5 comparison")
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original_seed = config_get_set_seed(200)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# define map operations
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transforms = [
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vision.Decode(),
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vision.RandomSharpness((10.0, 15.0))
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]
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# Generate dataset
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data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data = data.map(operations=transforms, input_columns=["image"])
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# check results with md5 comparison
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filename = "random_sharpness_cpp_01_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore configuration
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ds.config.set_seed(original_seed)
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ds.config.set_num_parallel_workers(original_num_parallel_workers)
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def test_random_sharpness_c_py(degrees=(1.0, 1.0), plot=False):
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"""
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Feature: RandomSharpness op
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Description: Test RandomSharpness with C and python Op
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Expectation: The dataset is processed as expected
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"""
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logger.info("Test RandomSharpness C and python Op")
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# RandomSharpness Images
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data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data = data.map(operations=[vision.Decode(), vision.Resize((200, 300))], input_columns=["image"])
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python_op = vision.RandomSharpness(degrees)
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c_op = vision.RandomSharpness(degrees)
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transforms_op = mindspore.dataset.transforms.Compose([lambda img: vision.ToPIL()(img.astype(np.uint8)),
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python_op,
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np.array])
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ds_random_sharpness_py = data.map(operations=transforms_op, input_columns="image")
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ds_random_sharpness_py = ds_random_sharpness_py.batch(512)
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for idx, (image, _) in enumerate(ds_random_sharpness_py.create_tuple_iterator(num_epochs=1, output_numpy=True)):
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if idx == 0:
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images_random_sharpness_py = image
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else:
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images_random_sharpness_py = np.append(images_random_sharpness_py,
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image,
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axis=0)
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data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data = data.map(operations=[vision.Decode(), vision.Resize((200, 300))], input_columns=["image"])
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ds_images_random_sharpness_c = data.map(operations=c_op, input_columns="image")
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ds_images_random_sharpness_c = ds_images_random_sharpness_c.batch(512)
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for idx, (image, _) in enumerate(
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ds_images_random_sharpness_c.create_tuple_iterator(
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num_epochs=1,
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output_numpy=True)):
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if idx == 0:
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images_random_sharpness_c = image
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else:
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images_random_sharpness_c = np.append(images_random_sharpness_c,
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image,
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axis=0)
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num_samples = images_random_sharpness_c.shape[0]
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mse = np.zeros(num_samples)
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for i in range(num_samples):
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mse[i] = diff_mse(images_random_sharpness_c[i], images_random_sharpness_py[i])
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logger.info("MSE= {}".format(str(np.mean(mse))))
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if plot:
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visualize_list(images_random_sharpness_c, images_random_sharpness_py, visualize_mode=2)
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def test_random_sharpness_one_channel_c(degrees=(1.4, 1.4), plot=False):
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"""
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Feature: RandomSharpness op
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Description: Test RandomSharpness with cpp op with one channel on MnistDataset (grayscale images)
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Expectation: The dataset is processed as expected
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"""
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logger.info("Test RandomSharpness C Op With MNIST Dataset (Grayscale images)")
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c_op = vision.RandomSharpness()
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if degrees is not None:
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c_op = vision.RandomSharpness(degrees)
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# RandomSharpness Images
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data = ds.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False)
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ds_random_sharpness_c = data.map(operations=c_op, input_columns="image")
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# Original images
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data = ds.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False)
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images = []
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images_trans = []
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labels = []
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for _, (data_orig, data_trans) in enumerate(zip(data, ds_random_sharpness_c)):
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image_orig, label_orig = data_orig
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image_trans, _ = data_trans
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images.append(image_orig.asnumpy())
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labels.append(label_orig.asnumpy())
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images_trans.append(image_trans.asnumpy())
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if plot:
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visualize_one_channel_dataset(images, images_trans, labels)
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def test_random_sharpness_invalid_params():
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"""
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Feature: RandomSharpness op
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Description: Test RandomSharpness with invalid input parameters
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Expectation: Correct error is thrown as expected
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"""
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logger.info("Test RandomSharpness with invalid input parameters.")
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try:
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data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data = data.map(operations=[vision.Decode(), vision.Resize((224, 224)),
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vision.RandomSharpness(10)], input_columns=["image"])
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except TypeError as error:
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logger.info("Got an exception in DE: {}".format(str(error)))
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assert "tuple" in str(error)
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try:
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data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data = data.map(operations=[vision.Decode(), vision.Resize((224, 224)),
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vision.RandomSharpness((-10, 10))], input_columns=["image"])
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except ValueError as error:
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logger.info("Got an exception in DE: {}".format(str(error)))
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assert "interval" in str(error)
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try:
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data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data = data.map(operations=[vision.Decode(), vision.Resize((224, 224)),
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vision.RandomSharpness((10, 5))], input_columns=["image"])
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except ValueError as error:
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logger.info("Got an exception in DE: {}".format(str(error)))
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assert "(min,max)" in str(error)
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if __name__ == "__main__":
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test_random_sharpness_py(plot=True)
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test_random_sharpness_py(None, plot=True) # Test with default values
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test_random_sharpness_py(degrees=(20.0, 25.0),
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plot=True) # Test with degree values that show more obvious transformation
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test_random_sharpness_py_md5()
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test_random_sharpness_c(plot=True)
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test_random_sharpness_c(None, plot=True) # test with default values
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test_random_sharpness_c(degrees=[10, 15],
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plot=True) # Test with degrees values that show more obvious transformation
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test_random_sharpness_c_md5()
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test_random_sharpness_c_py(degrees=[1.5, 1.5], plot=True)
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test_random_sharpness_c_py(degrees=[1, 1], plot=True)
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test_random_sharpness_c_py(degrees=[10, 10], plot=True)
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test_random_sharpness_one_channel_c(degrees=[1.7, 1.7], plot=True)
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test_random_sharpness_one_channel_c(degrees=None, plot=True) # Test with default values
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test_random_sharpness_invalid_params()
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